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Improving interpretability of deep learning models: splicing codes as a case study

Anupama Jha, Joseph K. Aicher, Deependra Singh, Yoseph Barash
doi: https://doi.org/10.1101/700096
Anupama Jha
1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania
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Joseph K. Aicher
2Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Deependra Singh
1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania
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Yoseph Barash
1Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania
2Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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  • For correspondence: yosephb@upenn.edu
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Abstract

Despite the success and fast adaptation of deep learning models in a wide range of fields, lack of interpretability remains an issue, especially in biomedical domains. A recent promising method to address this limitation is Integrated Gradients (IG), which identifies features associated with a prediction by traversing a linear path from a baseline to a sample. We extend IG with nonlinear paths, embedding in latent space, alternative baselines, and a framework to identify important features which make it suitable for interpretation of deep models for genomics.

Footnotes

  • https://majiq.biociphers.org/interpret-jha-el-al-2019/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 14, 2019.
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Improving interpretability of deep learning models: splicing codes as a case study
Anupama Jha, Joseph K. Aicher, Deependra Singh, Yoseph Barash
bioRxiv 700096; doi: https://doi.org/10.1101/700096
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Improving interpretability of deep learning models: splicing codes as a case study
Anupama Jha, Joseph K. Aicher, Deependra Singh, Yoseph Barash
bioRxiv 700096; doi: https://doi.org/10.1101/700096

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